Room pressure control apparatus having feedforward and feedback control and method

ABSTRACT

A controller for heating, ventilating and air-conditioning distribution systems, which includes a feedforward and feedback control strategies. The controller has a feedforward control strategy that generates a control signal based on control set points and identified characteristics of the system, and which adaptively adjusts such set points based on changes that are measured with respect to the identified characteristics. The controller is particularly adapted for controlling the differential air pressure in a controlled space relative to adjacent spaces.

CROSS REFERENCE TO RELATED APPLICATIONS

Temperature Control Apparatus Having Feedforward and Feedback Controland Method, filed Nov. 7, 1997, Ser. No. 08/965,961, by Osman Ahmed,John W. Mitchell and Sanford A. Klein.

FIELD OF THE INVENTION

The present invention generally relates to control systems, and moreparticularly to control systems that are used in heating, ventilatingand air conditioning fluid distribution systems.

BACKGROUND OF THE INVENTION

Fluid distribution systems associated with heating, ventilating andair-conditioning (HVAC) distribution systems are well known in the art,and are widely used in commercial applications, including apartmentbuildings and office buildings, for example. Such systems also seewidespread use in laboratory-type settings, and in such animplementation, the HVAC system must not only control the temperature ofthe air in the building, but must also exhaust potentially noxiousfumes, particularly if the building has a number of laboratory fumehoods in which experimental work is being carried out. Anotherimplementation that has additional important considerations in additionto controlling the temperature of the air in the building involves cleanroom environments where manufacturing of electronic integrated circuitsand the like is carried out.

In both of these latter implementations, the pressure of a room ofinterest may have to be controlled to be different from the space orrooms adjacent to the room of interest. In the case of the clean roomenvironment, the room of interest must be maintained at a differentialpressure higher than the surrounding space to insure that contaminatesdo not enter the room. In the case of the laboratory environment, theroom of interest is kept at a differential pressure less than thesurrounding area to contain any noxious fumes in the room.

To maintain a room of interest at a desired differential pressurerelative to the surrounding area, the HVAC system must be capable ofcontrolling the flow of air into the room, and the flow of air beingexhausted from the room, and must take into consideration any other airflow into and out of the room. Given the temperature controlrequirements that must be maintained in the room, it becomes a morecomplicated control problem that is not easily solved.

While variable air volume (VAV) control equipment has been used forproviding a control strategy for the implementations discussed above,and such control equipment has utilized a combination of feedforward andfeedback control methodology, there continues to be a need for aneffective control apparatus that provides improved performance, ease ofimplementation and cost effectiveness.

SUMMARY OF THE INVENTION

It is therefore a primary object of the present invention to provide animproved room pressure control apparatus having feedforward and feedbackcontrol strategies and a method of controlling such apparatus.

Another object is to provide such an improved controller which providessuperior performance, notable ease of implementation and significantcost effectiveness.

A related object is to provide such an improved controller whichincludes a feedforward control strategy that generates a control signalbased on control set points and identified characteristics of thesystem, and which adaptively adjusts such set points based on changesthat are measured with respect to the identified characteristics.

Still another object is to provide such an improved controller in whichthe feedforward control strategy uniquely employs the physical laws ofconservation of energy and mass to determine control set points that areemployed in the feedforward control strategy.

Yet another object is to provide such an improved controller whichutilizes a general regression neural network (GRNN) to identify thecharacteristics of the system, which results in simple, robust andexcellent capability in system identification, with minimalcomputational time.

Another object is to provide an improved control system which includessuch a feedforward process as well as a feedback process to generate acontrol signal, with the combination of such processes providingsuperior performance in many respects.

These and other objects will become apparent upon reading the followingdetailed description of the preferred embodiment of the presentinvention, while referring to the attached drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 generally depicts, in block diagram form, a controller embodyingthe present invention and also related control functionality.

FIG. 2 generally depicts, in block diagram form, one embodiment of thefeedforward control strategy identified in FIG. 1 that is employed forcontrolling a water heating coil and water flow control valve.

FIG. 3 generally depicts, in block diagram form, another embodiment ofthe feedforward control strategy identified in FIG. 1 that is employedfor controlling an air damper/actuator.

FIG. 4 generally depicts, in block diagram form, one embodiment of thefeedback control strategy identified in FIG. 1.

FIG. 5 generally depicts, in block diagram form, another embodiment ofthe feedback control strategy identified in FIG. 1.

FIG. 6 is a chart of normalized flow rates versus normalized controlsignals for a simulated valve having an authority a of 0.1 based onrepresentative smoothing factors σ for identification using the GRNNmethod.

FIG. 7 is a chart illustrating predicted heat load for a room usingvarious techniques.

FIG. 8 is a chart of normalized flow rates versus normalized controlsignals for a simulated valve based upon representative valveauthorities between 1 and 0.01 for identification using the GRNN method.

FIG. 9 is a chart comparing simulated and predicted control signals fora valve having an authority between 1 and 0.01 using the GRNN method.

FIG. 10 is a chart of normalized supply flow rates versus coileffectiveness for a simulated coil for identification using the GRNNmethod.

FIG. 11 is a chart of normalized flow rate versus normalized measuredcontrol signal for a damper for identification using the GRNN method.

FIG. 12 is a chart illustrating the operation of the pressure controlsequence for a fume hood exhaust application.

FIG. 13 is a chart illustrating the room differential pressure responsecomparing the performance of Models 1 and 2.

FIG. 14 is a chart illustrating the operation of the temperature controlcooling sequence for a fume hood exhaust application, and particularlyshows the rate of heat generation and flow rates versus time.

FIG. 15 is another chart illustrating the operation of the temperaturecontrol cooling sequence for a fume hood exhaust application, andparticularly shows the rate of rate of heat generation and flow ratesversus time.

FIG. 16 is a chart illustrating the operation of the temperature controlheating sequence for a fume hood exhaust application, and particularlyshows the rate of heat generation and flow rates versus time.

FIG. 17 is another chart illustrating the operation of the temperaturecontrol heating sequence for a fume hood exhaust application, andparticularly shows the rate of rate of heat generation and flow ratesversus time.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

Broadly stated, the present invention is directed to a controller andmethod of determining a control signal that uses a combined feedforwardand feedback control method for HVAC systems generally, and particularlyfor laboratory rooms. Although a laboratory room implementation will bespecifically described herein, the proposed control topology and resultsare valid for cleanroom applications where pressure is kept higher thanthe adjacent space to prevent any contaminants to flow into the room ofinterest from the outside.

The controller utilizes a combination of feedforward and feedbackcontrol as shown in the block diagram of FIG. 1, which includes threedistinct control loops, namely: a temperature control loop for heating,identified generally at 10; a pressure control loop embodying thepresent invention, indicated generally at 12; and a temperature controlloop for cooling, indicated generally at 14. The three loops arefunctionally interconnected as shown by lines 16, 18 and 20, and allloops are preferably implemented in a processing means, not shown, suchas a microprocessor or the like.

With regard to the pressure control loop 12 embodying the presentinvention, room pressure is typically controlled in terms of adifferential instead of an absolute value. The differential is definedas a difference between a reference space, i.e., an adjacent corridorand the room itself. For a laboratory room application, the goal is tokeep the differential pressure positive within a range of about 0.005 to0.05 w.c. This assures that the room pressure remains lower than theadjacent pressure under all operating conditions, and prevents air fromleaking into adjacent spaces. For a clean room application, the pressurein the room is maintained at a higher differential pressure thanadjacent space to prevent leakage into the room.

There are three common methods of room pressure control in use today,including direct pressure, flow tracking and cascaded control. Each ofthese schemes essentially modulates the supply flow in order to maintainthe room differential pressure. Hence, a simple sequence is consideredto assess the performance of different control methods for pressurecontrol. For a laboratory control application having fume hoods in theroom, a step change in the fume hood exhaust requires modulation of thesupply air flow to maintain the differential pressure set point.

The pressure control sequence used in the present invention is shown inFIG. 12. As indicated in FIG. 12, from a steady state condition, thefume hood exhaust jumps to a maximum value as the hood sash is opened.As a result, the laboratory room pressure decreases, which makes thedifferential pressure go higher. The control loop 12 then senses thedeviation between the actual differential pressure and the set point andopens the supply flow to return the set point.

With regard to the temperature control-heating loop 10, its controlsequence is shown in FIGS. 16 and 17. In most variable air volume (VAV)applications, the supply air that is fed into a laboratory space has aconstant temperature of about 55° F. Based on the normal design coolingload, the supply volumetric flow rate is selected to maintain thespecified room temperature, usually a value between about 70 and 75° F.To maintain the differential pressure it is necessary that the minimumtotal laboratory exhaust exceed the supply flow rate due to the fumehood sash opening, the supply flow rate also increases accordingly. Thenew supply flow rate at a constant 55 degree F. may exceed therequirement of the cooling demand. The room temperature therefore, maydrop below the set point. This sequence requires the local reheat valveto open and increase the supply air temperature to keep the roomtemperature set point. The coupling between room pressure and thermalconstraints is complex.

With regard to the temperature control-cooling loop 14, its controlsequence represents temperature control as a result of cooling needs.The rate of internal heat generation is the primary disturbing forcethat activates this sequence. The internal rate of heat generation canincrease by many fold due to other activities in a laboratory such asautoclaves, ovens and occupancy. When the internal generation suddenlyincreases, the room temperature rises. The only cooling source availableis the supply air stream at 55° F. However, the supply flow cannot beincreased unless the exhaust flow is also increased in order to maintainthe differential pressure constraint. But the laboratory exhaust flowcannot be increased because that will upset the laboratory roompressure. To circumvent this problem, another source of the exhaust,i.e., the general exhaust, is opened to allow an increased supply flow.As is shown in FIGS. 14 and 15, by artificially increasing the totallaboratory exhaust, both room temperature and the pressure set pointsare maintained.

Each of the control loops 10, 12 and 14 has feedforward blocks 22 and/or24 which are shown in the block diagrams of FIGS. 2 and 3, respectively.The block diagram of FIG. 2 is for the feedforward control of a heatingcoil, while the block diagram of FIG. 3 is for the feedforward controlof a damper which may be used in the temperature control-heating loop,temperature control-cooling loop and for a pressure control loop.Similarly, feedback blocks 26 are identified in the control loops 10, 12and 14 and the block diagram for each of these feedback blocks is shownin FIG. 5 or FIG. 4.

It should be understood that the block diagram of FIG. 2 includes aphysical system block 28 which is intended to diagrammatically show coiland valve actuator 32, and temperature sensors, that are used in theoperation of the controller, and which are described hereinafter indetail. Also, the block diagram of FIG. 3 has a physical system block 29which is intended to diagrammatically show the pressure measuring meansand flow measuring means that are used in the operation of thecontroller. Similarly, control loops 10, 12 and 14 have a supplydamper/actuator block 30 which is intended to diagrammaticallyillustrate the supply damper and actuator associated with the air supplyduct to the room that is being controlled. Also, control loop 10 has acoil/valve actuator block 32 that is intended to diagrammaticallyillustrate the circulating water heating coil and water valve forcontrolling the flow of water through the heating coil, it beingunderstood that the coil is located in the air supply duct so that thecoil is adapted to heat the air passing through the supply duct.Finally, control loop 14 has a general exhaust damper/actuator block 34that is intended to diagrammatically illustrate the general exhaustdamper and actuator associated with the air general exhaust duct of theroom that is being controlled. It should be understood that the generalexhaust duct is separate and distinct from the exhaust duct or ductswhich are connected to laboratory fume hoods that are present in theroom, and which exhaust air together with fumes and the like from theinterior of the fume hoods. Such fume hood exhaust will necessarilyremove air from the room, and the controller will compensate for suchexhausting as will be described.

With respect to the operation of the feedback control block 26, andreferring to FIG. 5, it employs a Proportional-Integral-Derivative (PID)control method, as is known to those skilled in the art in the HVACindustry. The feedback controller uses the error between the set pointand the measured variable as its input and the PID control is used toreturn the process variable to the set point. A simple digital versionfor the control signal C_(s),m from a PID can be developed starting witha discrete expression for PID at m^(th) sample time as follows: ##EQU1##where S_(t) =sample time; P_(g), I_(g) and D_(g) are proportional,integral and derivative gains. The first term on the right hand side ofthe equation represents a constant offset. The second term isproportional action factor, the third term is an integral action factorand the last term is a derivative action factor.

A similar expression can be written for m-1^(th) sample as, ##EQU2##Now, by subtracting latter equation from the former, the followingequation can be obtained which is easy to implement in a digitalcontroller. ##EQU3## With respect to the feedforward control, physicalmodels are used to determine the set points for control variables; i.e.,the supply air flow rate and supply air temperature and the generalexhaust damper. The selection of a particular control variable is basedon the application. An application is defined as a sequence of eventsinitiated by a disturbance in a process variable; i.e., the laboratoryroom pressure and temperature which requires the controller to respondin order to change the state of a control variable. For example, if thelaboratory room total exhaust suddenly increases due to the hood sashopening, the room pressure will decrease. Hence, the supply flow ratehas to be increased in order to keep the room pressure at its set point.In this example either the total laboratory room exhaust flow ordifferential pressure across the laboratory room is a process variable,depending upon which one is measured, whereas the supply flow rate isthe control variable.

The second step of a feedforward controller involves generating controlsignals based on the set point determined in the first step and the HVACequipment characteristics. In a variable air volume (VAV) laboratoryroom HVAC system, two types of control equipment are commonly found.There would typically be a valve or a damper which restricts the flow ofwater or air followed by a water-to-air coil which heats up thelaboratory room supply air. The characteristics for each componentcorrelate input variables to the output as a control signal.

As shown in FIG. 2, the feedforward controller 22 has an on-lineidentification block 36 and a control block 38, and the controller 24shown in FIG. 3 similarly has an on-line identification block 40 andcontrol block 42. The identification blocks 36 and 40 capture and updatethe process characteristics based on the process input control signalsand the measured variables. The identification blocks 36 and 40 pass theupdated characteristics periodically to their respective control blocks38 and 42 for control action.

In this context, it should be understood that in a sense, thefeedforward controller has a "feedback" mechanism to compensate as thesystem characteristics change. However, this is different from afeedback control where the measured process variable is compared withits set point to generate the error signal and the output signal isessentially a function of this error signal. In the feedforwardidentification process, the process variable and even the systemdisturbance are measured if it is cost effective and feasible. Thefeedforward control blocks 38 and 42 act upon receiving a set pointsignal and provide a control signal based on the identifiedcharacteristics of the process. The essence of a feedforward control isto generate the control output in response to a change in the set pointof a process or measured variable. Since the feedforward control doesnot need an error to generate the control signal, it responds fasterthan the feedback control.

The identification process captures the system characteristics over theentire operating range which makes the controller robust. If theidentification scheme were able to capture the system characteristicsperfectly, there would be no need for the feedback controller. However,perfection cannot be achieved without incurring a major cost due toerrors, noise, and accuracy in the data. Thus, the feedback control isrequired to compensate for the steady state error or offset.

For each piece of control equipment in a VAV laboratory room HVACsystem, the feedforward controller is capable of generating a controlsignal in response to a set point change of a process variable. Thephysical process associated with each component is needed in order tounderstand how the control signal can be generated.

The physical process of heating a room involves two components: avalve/actuator assembly and the heating coil. A VAV laboratory willcommonly have a heating coil, a valve/actuator and damper actuators inorder to satisfy both pressure and temperature requirements in thelaboratory. The valve/actuator characteristics are similar to those of adamper/actuator used to modulate air flow rate in a HVAC airdistribution system. Therefore, the process described here for the valveis equally applicable to dampers and actuators. By choosing an exampleof a heating process, the identification of all HVAC components in a VAVlaboratory can be illustrated.

The water flow rate through the valve will depend on the valve open areaand the authority, a. The authority is defined as the ratio of pressuredrop across the valve to the overall circuit pressure drop when thevalve is fully open, or for each valve, ##EQU4## Expressing the valvecharacteristics in terms of authority, percent valve open and percentmaximum flow rate is typical in the art (ASHRAE 1992).

For a single circuit system, in practice, the circuit pressure drop willbe small compared to the valve which will cause the authority, a, to beclose to 1.0. However, for a system with multiple circuits, the pressureloss in the main segment becomes significant compared to the branchsegment as the distance between the pump and the coil increases. As aresult, the value of authority varies depending upon the ratio ofpressure losses as indicated in the authority equation. The authority ofany circuit is time dependent because the flow in each circuit varieswith the time. The valve authority can be calculated either using thebasic relations between design pressure drop and flow rate or bymeasuring static pressures at the pump outlet and valve inlet at thedesign flow conditions and calculating authority at any time.

As shown in FIG. 2, a control signal C_(s) is generated based on theheating demand and is sent to the valve/actuator 32 to open or close thevalve. The heating coil has physical inputs of water and air flow ratesand inlet air and water temperatures. The coil outputs are watertemperature and air outlet temperature. Since the water outlettemperature is not directly linked to the control of supply air thermalenergy, it is not considered in the identification process. Instead, Ris used as a non-dimensional variable combining the water inlettemperature, T_(f),i, and air inlet and outlet temperatures, T_(a),i andT_(a),o respectively. Both T_(f),i and T_(a),i are either knownconstants for a given system as user input parameters or are measuredand input to the controller. The dimensionless variable R, which canalso be viewed as coil effectiveness, is a measure of the heatingsupplied. R can be expressed as

    R=(T.sub.a,o -T.sub.a,i)/(T.sub.f,i -T.sub.a,i)            (5.5)

The physical process described above relates the system processvariables as a function of the control input. The process needs to beinverted when used in a feedforward controller to produce the desiredcontrol signals that set the valve at the desired position in responseto the water flow rate set point.

This control scheme can be explained in connection with FIG. 2. Theorder of the physical heating process previously described is reversedin the feedforward block shown in FIG. 2. The feedforward block isactivated upon receiving a signal of coil outlet air temperature setpoint, T_(a),o|sp. The on-line identification normalizes and inverts thecharacteristics to produce the desired control signal. The coilcharacteristic is utilized first in the control process to yield thedesired water flow rate, ν_(f), for the desired coil outlet airtemperature set point, T_(a),o|sp and for given supply air flow rate,ν_(s). Knowing the water flow requirement and the authority, a, theidentified valve characteristic then generates a control signal, C_(s).

The observed variables from the system along with the control signal,C_(s), may be periodically collected and used to update the coil andvalve characteristics by a separate identification scheme that isindicated as an on-line adaptive identification in FIG. 2. The observedvariables could include T_(a),o, T_(a),i, ν_(f) and ν_(s). However,instead of an expensive means of measuring water flow rate, the coiloutlet water temperature, T_(f),o can be measured and ν_(f) can becalculated using the following energy balance.

    ν.sub.f =Kν.sub.s (T.sub.a,o -T.sub.a,i)/(T.sub.fi -T.sub.f,o)(5.6)

where K is a constant that is determined empirically and expressed as aratio of the products of the mass-capacitance of air and water or,##EQU5## where ρ_(a) =density of air; ρ_(f) =density of fluid; c_(a)=capacitance of air; and c_(f) =capacitance of fluid. The above waterflow rate (ν_(f)) equation is preferred as a way to calculate the waterflow rate through the local heating coil considering cost andpracticality as opposed to measuring flow directly. The HVAC controlsystem usually trends the air flow rate through the coil as well as thedischarge air temperature for control purposes. The values are updatedevery second or more. Often the values for coil air and water inlettemperatures are also available from the central air handling unit andchiller plant. Thus, by adding a water temperature sensor, the coilwater flow rate can be estimated using the water flow rate (ν_(f))equation. This is a cost effective proposition since flow sensor costsmore than a temperature sensor and such cost difference becomessignificant considering the large number of local heating coils presentin a building. Also, in a retrofit application, a strap-on temperaturesensor can be installed outside the pipe to avoid costly jobinterruption. On the other hand, most types of flow sensor need to beinserted inside the existing pipe which interrupts the system operation.

A few additional factors favor the use of temperature sensor. First, thewater flow rate equation will be only used for identification purposes.Hence, dynamic data are not needed to solve for water flow rateequation. Instead, only periodic steady state data are needed, whichshould not be difficult to obtain given the preferred sample rates of 1or more per second. Secondly, the governing relationships between thewater flow rate and air flow rate and air and water side differentialtemperature across the coil are important in estimating the coil waterflow rate. The absolute accuracy of each measurement is therefore notcritical. Finally, the purpose of the feedback controller in a combinedfeedforward and feedback approach is to compensate for inaccuracies withthe identification process which include measurement error. Hence,accurate measurement for identification is not required.

FIG. 2 clearly demonstrates the need for estimating the coil outlettemperature set point T_(a),o|sp before the feedforward block canproduce any control signal to the valve. In fact, the inverse of heatingcoil characteristics will generate a set point for water flow ratethrough the valve. By knowing the valve authority and water flow rateset point, the controller will then be able to generate a control signalto the valve.

The process described for a valve is similar to that for a damper and isshown in FIG. 3. In the case of a damper, the signal will be generatedin response to the demand for air flow rate. The air flow rate set pointis determined first, which along with the damper authority is used bythe feedforward block to generate the control signal.

In accordance with an important aspect of the present system, a methodis needed to determine the set points for the supply air flow rate andsupply air temperature and general exhaust air flow rate. The supply airflow rate set point is coupled to the pressure loop for laboratory roomsafety. The supply air temperature set point is determined when the roomtemperature falls below the set point and heating is needed. The generalexhaust is opened when the room becomes hot and the temperature exceedsthe set point. In all cases, physical models are used to calculate theset points.

To determine the supply flow set point, the steady state mass balanceand infiltration equations can be used to solve for the supply flow setpoint. The steady state mass balance, when written in terms of setpoints, is (mass balance) ##EQU6## The infiltration relation, the amountof air entering the room from other than the supply duct, is:

    ν.sub.ad|sp =K.sub.1 (ΔP.sub.|sp).sup.n(5.9)

The laboratory room pressure differential, ΔP.sub.|sp, is defined as adifferential as follows:

    Δp.sub.|sp =P.sub.ref|sp -P|sp(5.10)

There are nine variables in the above mass balance equation comprisingthe temperature, flow rate and pressure of three air streams: supply,infiltration and laboratory room exhaust. The room set points fortemperature and pressure infiltration are known. The volumetric flowrate of infiltrating air at the set point, ν_(ad)|sp, is also known fromthe ν_(ad)|sp and ΔP.sub.|sp equations. Similarly, the supply airpressure, P_(s)|sp, room pressure, P.sub.|sp, and temperature,T.sub.|sp, set points are given from design data. There are threeunknowns: laboratory room supply air flow rate, ν_(v)|sp ; totallaboratory room exhaust set point, ν_(e)|sp ; and supply air dischargetemperature set point, T_(s)|sp. The total laboratory room exhaust is asum of general exhaust and exhaust from fume hoods and given by:

    ν.sub.e|sp ν.sub.fh|sp +ν.sub.ex|sp(5.11)

In a VAV laboratory room, the fume hood exhaust set point is a knownquantity for each position of the fume hood sash. Hence, by determiningthe set point for total laboratory room exhaust, the general exhaust setpoint will be known.

In order to solve for either supply air discharge temperature or generalexhaust set point, the following steady state energy equation is used inaddition to the four immediately preceding equations. The steady stateenergy equation is: ##EQU7## where c_(f) is a unit conversion factor.

It should be understood that when the supply air discharge temperatureset point is to be determined, then the general exhaust is usually aknown quantity and vice versa. The need for determining the desiredsupply air discharge temperature arises when the fume hood exhaustsuddenly increases as the sashes are opened. The increase in exhaustmeans more supply air is required to maintain the room pressuredifferential. However, the room will be overcooled if the quantity ofsupply air, typically at 55° F., exceeds the amount required to offsetthe cooling load in order to maintain room temperature at 70° F. Toprevent room overcooling, the supply air must be heated and the heatingcoil valve controlled in order to achieve a desired supply airtemperature set point.

The general exhaust is needed when fume hoods are closed and the rate ofinternal heat generation is increased due to process or equipmentoperation. The room, under such situations, needs more cooling. However,just additional cooling by means of an increase in volumetric flow rateof 55° F. supply air will upset the room pressure equilibrium. As aresult, the general exhaust damper is opened to allow more supply air toprovide added cooling. The controller has to determine and control thegeneral exhaust flow rate and supply air flow rate in order to maintainthe room pressure and temperature set points. In this case, of course,the supply air temperature at 55° F. is fixed. When heating is requiredthe general exhaust damper is usually closed which means that ν_(e)|spequals zero.

Hence, the use of the five preceding equations yield a set pointsolution for a combination of supply air flow rate and temperature orsupply and general exhaust flow rates depending upon the controlsequence. In the last equation, the steady state energy equation, thespace thermal load, q_(load), needs to be determined in order to obtainthe set points. The transient room load is approximated as proportionalto the first order derivative of room temperature with respect to time.This is the internal energy storage term assuming the mass of air in thelaboratory room remains constant.

    q.sub.load|tr =ρc.sub.v dT/dt                 (5.13)

The room temperature, T, can be measured directly by placing thetemperature sensor in the room exhaust duct instead of following theusual practice of mounting a wall room thermostat. In many laboratories,the exhaust from the fume hoods and the laboratory room are ductedtogether and the common intersection between the two exhaust streamsprovides a good location for a duct temperature sensor. Due to the highventilation requirement, the air in a laboratory room is well mixed andtherefore, exhaust air temperature is a good representation of the roomtemperature, T. In certain situations, however, it is not feasible toinstall a duct temperature sensor due to the fear that the electricalvoltage supplied to the sensor may react with the volatile fumes. Underthose situations, the room wall thermostat sensor can be still used andthe room temperature can be estimated by simplifying the followingequation and using a temporary room air temperature sensor as explainedbelow. ##EQU8## This equation couples both the panel wall and the roomair temperature to the thermostat temperature, T_(st). The couplingbetween the panel wall and the thermostat temperature is necessary sincethe radiant wall heats and cools the panel wall on which the thermostatis mounted. In most laboratory rooms, the wall temperatures will be veryclose to the space temperature since both laboratory room and thelaboratory room adjacent spaces are usually interior zones and aremaintained at the same temperature. As a result, the above equation canbe simplified as ##EQU9##

The only thermostat calibration constant, C2_(st), can be easily foundduring commissioning process by locating a temperature sensor in theexhaust duct temporary or at a good location within the room, changingthe room temperature set point, trending both thermostat temperature,T_(st), and room air temperature, T, from a temporary location andfitting trended data to the above equation to determine C2_(st). Oncethe thermostat constant is calibrated, the temperature sensor can beremoved from the temporary location. As an alternative, if feasible, thesensor to measure the room air temperature can be located in the generalexhaust duct for the laboratory room air only. The sensor in the generalexhaust duct cannot be used continuously in lieu of the thermostat sinceoften the general exhaust damper may be closed completely and the sensorwill not be exposed to the room air flow. On the other hand, by having asensor in the general exhaust, the calibration process can be automatedto update the value of the calibration constant, C2_(st), routinely byusing the trended sensor and the thermostat values in equation 5.15 whenthe general exhaust flow is significant.

When the room temperature is steady, the total cooling load can bedetermined by using the following energy equation, which relates theload to the total laboratory room exhaust flow rate, room temperatureand the supply flow rate at the preceding time step, t-1. The airdensity is assumed to be constant and identical for supply, exhaust andinfiltration air.

    q.sub.load|ss =ν.sub.e,(t-1) ρc.sub.p T.sub.(t-1) -ν.sub.s,(t-1) ρc.sub.p T.sub.s -ν.sub.ad|sp ρc.sub.p T.sub.ad                                     (5.16)

The total laboratory room exhaust is expressed as a sum of generalexhaust and fume hood exhaust flows,

    ν.sub.e =ν.sub.s,(t-1) +ν.sub.ad|.sbsb.sp(5.17)

In both of the above equations, the infiltration flow rate set point,ν_(ad)|sp, is used instead of actual infiltration flow rate, to avoid anoscillation in the room load prediction. The transients in ΔP willintroduce oscillation in both infiltration flow rate, ν_(ad), and roomtemperature, T. As a result, the calculated room cooling load willoscillate.

In order to see the effects of transient ΔP and ν_(ad) on the calculatedload, a simulation is performed by selecting a simple control strategy.The room pressure and temperature responses are obtained by increasingthe room internal heat generation rate from a steady value of 82.50Btu/min to 412.50 Btu/min. As the room temperature increases due to thehigher rate of internal generation, the room calls for more cooling.

Additional cooling can be only provided by increasing the flow rate ofsupply air at 55° F. However, before the supply air flow rate isincreased, the total laboratory room exhaust has to be increased tomaintain the room pressure differential, which in turn requires thegeneral exhaust to be increased. The use of the infiltration flow rateset point in predicting the load is found to work since the objectivehere is determine the required supply air flow rate, temperature orgeneral exhaust flow rate in order to achieve room pressure differentialand temperature set points. Essentially, the controller drives thesupply and general exhaust dampers to maintain the room pressuredifferential of 0.05 w.c. and room temperature of 70° F. The controllerfirst calculates the set points for supply and general exhaust flows atthe steady state conditions before and after the increase in the rate ofinternal heat generation takes place.

Based on the flow set points, the controller determines the damperpositions using the identified relationship between flow rate throughthe damper and the damper position. The purpose of using a simplesimulation is to illustrate that the pressure and temperature transientscause, in turn, transient behavior in infiltration flow rate. Theresultant effect is that the predicted load will follow the transientchanges in infiltration flow rate and which are oscillatory. Theinstantaneous load under steady state condition is determined applyingthe foregoing q_(load)|ss equation which uses the actual totallaboratory room exhaust, ν_(ex). In contrast, the predicted steady stateload, q_(load)|ss using the set point follows the actual load veryclosely during the transient, and agrees with the simulated load whichincludes both steady state heat generation and the wall effect. Theq_(load)|ss at steady state uses q_(load)|ss equation which calculatesν_(ex) assuming a set point for ΔP. As a result, the set point forq_(load)|ss corresponding to a ΔP set point of 0.05" w.c. is used inequation 5.16. For the selected control sequence, the difference inactual and as determined by equation 5.16 is found to be about 41 cfmwhich translates into a difference of about 43 Btu/min between theinstantaneous load and q_(load)|ss under the steady state.

Based on the observations, the predicted steady state load is selectedfor use in simulation instead of the instantaneous load. The controlleralso does not need to follow the actual instantaneous room load as thatwill cause the dampers to oscillate. The use of the predicted load basedon set points will provide a stable control state.

When the room needs cooling, both the storage and steady state loadterms are added to compute the load, q_(load)|ss, in order to determinethe general exhaust and supply flow rate set points. In the case ofheating only, however, the storage term is neglected to compute incalculating the supply air temperature set point.

The identification process produces component outputs based on input,output and information related to other variables using the identifiedcomponent characteristics. There are two types of components which needto be identified: a heating coil and a valve/damper. However, since thephysical characteristics will be inverted in the control process aspreviously explained, the identification process should capture therelationship between the inputs and outputs of the inverted physicalprocesses. For example, for a heating coil, the inputs are anondimensional variable, R and fixed variables T_(a),i, coil inlet airtemperature and fluid inlet temperature, T_(f),i. The coil output willbe the water flow rate through the coil, ν_(f).

Similarly, referring to FIG. 2, the identification of an invertedphysical process for a damper or valve involves flow rate and authorityas two inputs and control signal as an output. A damper or a valve isessentially a variable fluid resistance device. Both exhibit similarfluid characteristics and their performance is expressed in terms ofidentical variables and, hence, can be represented by the same models.

The General Regression Neural Network (GRNN) is chosen to identify thecoil and valve characteristics due to its simplicity, robustness andexcellent capability in system identification. Unlike a conventionalneural network, it requires minimal computational time to effectivelycapture the system characteristics. The following is only a briefaccount of GRNN to illustrate its implementation in identification ofthe components.

The input to a GRNN is a series of data that can be in multipledimensions. For sample values of X_(i) and Y_(i) of input vector X andthe scalar output Y, an estimate for the desired mean value of Y at anygiven value of X is found using all of the sample values in thefollowing relations: ##EQU10## where the scalar function D_(i) ²,representing the Euclidean distance from the given value to the knownpoints, is given by

    D.sub.i.sup.2 =(X-X.sub.i).sup.T (X-X.sub.i)               (5.19)

and σ is the single smoothing parameter of the GRNN. The above equationsare the essence of the GRNN method. For a small value of the smoothingparameter, σ, the estimated density assumes non-Gaussian shapes but withthe chance that the estimate may vary widely between the known irregularpoints. When σ is large, a very smooth regression surface is achieved.The Holdout method (Specht 1990) is used to calculate the value ofsmoothing parameter, σ.

The implementation of GRNN to the characteristics of a heating coil orvalve/damper also offers advantages over the conventional methods ofidentification. In a traditional regression method for identification,the operator has to input a priori knowledge of the equation type or hasto search for the best fit equation exhaustively. The code requirementfor a nonlinear regression is intensive and may be prohibitive foreffective on-line use. In contrast, the GRNN does not require any userinput for the functional form of the characteristics and uses astrikingly simple code. Moreover, the GRNN algorithm can be imbeddedinto a neural hardware processor, thereby eliminating softwaredevelopment process to a large extent since software coding during fieldinstallation is not necessary.

For a heating coil, the input vector X contains dimensionless variable Rand ν_(a)|sp while the output, Y, is water flow rate through the coil,ν_(f)|sp. Using valve authority, a and ν_(f)|sp as input, the valve GRNNthen produces an output of required valve control signal, Cs. For adamper/actuator for flow control, the input and output variables are thesame as that for a valve.

In accordance with another important aspect of the controller, coil andvalve characteristics are generated using the models described above,and subsequently used in the GRNN to identify the characteristics. Thephysical variables are first normalized. Besides R (Equation 5.5) andauthority, a, whose range is from 0 to 1, other normalized variablesused are ##EQU11## In this example, the values of C_(smax), ν_(fmax) andν_(smax) are 1.0, 2500 cf (1180 L/s) and 1.0 gpm (0.0631 L/s),respectively. Using the value of R required to meet the load and a givenvalue of nν_(s), a value of nν_(f) can be determined which can besubsequently used in a valve model along with the given authority togenerate a control signal, nc_(s), as indicated in FIG. 2. The coil andvalve characteristics data in Table 5.1 are generated using normalizedvariables and the models described above.

The GRNN method can be best explained by using an example of regressingvalve data for a constant authority. For example, choosing authority ato be 0.1, a nonlinear relation, shown in FIG. 6 is established betweenthe normalized control signal and normalized flow. For a constantauthority, there is only one input and the vector X in the above scalarfunction equation becomes a scalar series of normalized flow rate,nν_(f). In the scalar function equation, the function D_(i) ² can becomputed where X_(i) is the ith sample in the series. The GRNN equationfor Y(X) can then be solved using D_(i) ², and corresponding Y_(i) asthe ith sample of nc_(s) in the identification data.

                  TABLE 1                                                         ______________________________________                                        Valve Simulation Parameters                                                    = .00001; W.sub.f = 1; K.sub.cd = .08641 (64.89; k.sub.0 = .042              (31.54);                                                                      Authority                                                                                ##STR1##          Maximum υ'.sub.f  gpm                    ______________________________________                                                                     (L/s)                                            1.00      -.086 (-64.58)     3.00 (0.1893)                                    .70       -.034 (-25.53)     2.50 (0.1577)                                    .50        .037 (27.78)      2.12 (0.1337)                                    .20        .407 (305.63)     1.34 (0.0845)                                    .10        1.02 (765.97)     0.95 (0.0599)                                    .05        2.25 (1689.64)    0.67 (.0423)                                     .01       12.13 (9109.02)    0.30 (.0189)                                     ______________________________________                                    

The simulation of coil and valve characteristics as well as GRNN isperformed using the Engineering Equation Solver (Klein and Alvarado1997) which is specifically incorporated by reference herein. Thesimulated data in FIG. 6 are shown by the solid line while the pointsare generated by using the GRNN equation for various smoothing parameter(σ) values. Although smaller values of σ seem to represent the databetter, overfitting by choosing a very small σ should be avoided. Thesimulated data contain fourteen samples obtained by varying nCs from 0.0to 1.0 in increments 0.1 and nCs of 0.05, 0.15, 0.25.

The Holdout method, (Specht 1990) which is specifically incorporated byreference herein is used to calculate the optimum value for sigma, σ,and it is found to be 0.01. The effect of choosing a higher value of σis apparent in FIG. 6. With the larger value of σ of 0.5, a smoothnearly linear trend is found that differs significantly from the inputwhile with smaller values, the GRNN attempts to approximate all samplesand is not smooth between points. For σ=0.01, the average error betweenthe predicted and simulated signals is found to be 2.62% while themaximum error of 14% is observed for the lowest value of control signalthat is not included in the identification data (nC_(s) of 0.35). Aslight error is also observed at the higher value of nν_(f), since thecontrol signals becomes highly sensitive to the normalized flow rate.

However, the relative error at the higher end of the valve curve is muchsmaller compared to the lower end due to the higher absolute value ofcontrol signal at this end. The sample size and the choice of samples,therefore, are important variables along with the smoothing parameter,σ. In fact, by including the sample of nC_(s) =0.35 in theidentification data, the error between the simulated and the predictedcontrol signal for that specific sample can be decreased from 14% toless than 1% while the average error can be dropped from 2.62% to 1.31%.In order to identify damper/valve characteristics, only 200 samples atmost will be required to cover the entire range of operation. This isbased on the assumption that the authorities can be varied between0.001, 0.01, 0.05 and 0.1 to 1 in increments of 0.10 while the controlsignal can be varied between 0.05, 0.075, 0.01, 0.15, 0.20, 0.25, 0.30,0.35 and 0.40 to 1.0 in increments of 0.1. Any state-of-the-art localcontroller will be able to process the 200 sample values with ease andspeed. In reality, however, the total number of points to cover theactual operating range will be much less, i.e., preferably less than100.

A range of valve authorities between 0.5 and 0.1 was chosen to test theGRNN method. Again, the Holdout method is used to determine the optimumsmoothing parameter σ which is now 0.05, and which produces a sum ofsquare error of 0.189 over a identification data size of 30 samples. Theidentification data set includes values of authority of 0.10,0.30 and0.50 and nc_(s) between 0.10 to 1.0 equally spaced. The test data setvaries nc_(s) from 0.05 to 0.95 in increments of 0.10 and also includesintermediate authorities of 0.20 and 0.40. The average error of about3.0% is low compared to the range of the data set. Some errors higherthan the average are found for higher values of control signal where thecurve becomes very steep with the normalized flow rate,.

The operating range for the valve or damper is typical of these controlapplications. Hence, the method of using GRNN to representcharacteristics using a small data set has demonstrated promise andimplementable in a real controller on an on-line basis. In a realapplication, operating characteristics over the entire operating rangecan be developed during commissioning by varying the damper open area.Once captured, the operating characteristic will be stored in thefeedforward controller and control signal will be generated based thestored data using GRNN. The time and effort required to tune thefeedback loop will decrease as the error signal for the feedback loopwill always have a low value. Reduction of commissioning cost and timeand enhancement of system performance are the two major factors infavoring combined feedforward and feedback controller for a buildingHVAC distribution system.

The measured data obtained during the commissioning process will be usedonly to initialize the identification process. As the system operatesand more operating data are collected, the identification will beupdated accordingly. The essence of combined feedforward and feedback isto generate a rough estimate of the control signal with the feedforwardblock while the refinement is made with the feedback. In fact, thefeedforward block also has a feedback mechanism that updates theidentification. However, the identification process is kept separatefrom the control process for ease of implementation and costeffectiveness.

Another method for implementing GRNN in a controller is to generate thecharacteristics using the simulated data. The characteristics can bestored and updated as the real data become available and replaces thesimulated data.

FIG. 8 shows both the identification and the test data covering theentire operating range of a valve. These were obtained by simulatingcontrol signals that varied between 0.1 to 1.0 for each authority in theidentification set over which the authorities vary from 0.01 to 1.0.Also, additional samples are duplicated from the test set to theidentification set at low values of authority and control signal. Intotal, 160 samples are used in the identification set while 150 samplesare included in the test set. The Holdout method using a smaller dataset with authorities of 0.01, 0.10, 0.25, 0.50, and 1.0 is used tooptimize the value of σ. A smaller data set having sparse values stillyields a good choice of σ of 0.01 for the data set shown in FIG. 8.

The plot comparing simulated and predicted control signals is shown inFIG. 9. Again, higher than average errors occurs for large controlsignals as well as for low authorities. The large error for a specificsample can be vastly decreased by including that sample in theidentification set. This can be easily achieved in a real controller bycomparing the control signal sent to the valve and the damper and thecontrol signal generated by the feedforward control signal. If thedifference between the feedforward and the total control signalincreases more than a predetermined fixed threshold value, the controlsignal and corresponding normalized flow rate, and the authority can beput back into the identification set.

Finally, the GRNN is used to identify the characteristics of a heatingcoil. Referring to FIG. 2, the GRNN needs to predict the required waterflow rate through the coil for given R and air flow rate. For randomlyselected values of normalized supply air flow rate nν_(s) and R, thenormalized flow rates, nν_(f) are calculated using energy balanceequation for ν_(f), the mass-capacitance equation for K, and thenormalizing equations for finding nCs, nν_(s), and nν_(f). A portion ofthe simulated data is used for identification purpose while the rest isset aside to test the GRNN algorithm. The test samples are purposelychosen as to cover the entire operating range. FIG. 9 shows both theidentification and the test data.

An average error of 2.6% between the predicted and simulated normalizedflow rates was found. Unlike the valve in which a definite pattern isevident, the coil plot in FIG. 10 appears random. Even with such sparseand random distribution of input data, the GRNN is able to predict thecoil flow rates with good accuracy.

In addition to the simulated data, measured damper characteristics arealso used to test GRNN. Two sources were used to obtain the measuredvalues: 1) Test data taken to calibrate damper performance and 2) Activedamper performance at a job site using a building automation system(BAS). In the first case, damper curves are experimentally generated forthree damper authorities as shown in FIG. 11.

The test sensors used to obtain data are similar to those used incommercial building control systems. For a given control signal, theflow rate through the damper is noted and normalized using thenormalizing equation. The GRNN is identified using the measured valuesof the control signals, flow rate and authorities while intermediatepoints on the authority curves are used to test the GRNN as shown inFIG. 11.

Compared to the simulated data, the measured curves in FIG. 11 exhibitmore randomness as expected. At low flow rates, the three authoritycurves converge into a single one indicating the difficulty of measuringflow rate when the damper is barely open. At high flow rates and lowvalues of authority, increasing the control signal will not increase theflow. The GRNN predicted the measured values with an average accuracy of4.30% which is good considering the error associated with themeasurement and data collection system. The Holdout method is used todetermine the optimum smoothing parameter, σ of 0.066. The errorincreases with the higher flow rate as the authority curves becomehighly sensitive as can be seen from FIG. 11. The range of the test datafor GRNN chosen in the normal operating range of the damper between 10%to 100% of flow rate.

For the damper at the job site, the authority remained unchanged at 7%during the data collection. For the same flow rate, the damper controlsignal varied over a wide range at both high and low flow rates. TheGRNN output is tested for each sample observation that has been used inthe identification data. Pre-processing of the raw measured values isnot used before the data are fed to the GRNN for identification. Apre-processing filter could be used on measured values to reduce theuncertainty with the measured values.

The accuracy of GRNN in predicting control signals has been shown to bewithin 6%. A linear regression of valve characteristics has also beenshown to yield an average error of 7%. The essence of the GRNN method isthe capability of predicting both nonlinear as well as linearcharacteristics without any user input for a fixed smoothing parameters.In the case of a regression tool, significant user input to specify theform the regression is required which often limits the actual on-lineimplementation of regression analysis for identification. Therefore, theresults demonstrate that the performance of GRNN exceeds that of linearregression.

The feedforward-feedback combination topology enables the majority ofthe control signal to be provided from the feedforward block such thatthe feedback block only deals with a small steady state error and thusrequires little tuning. Unlike the feedback loop, the feedforward loopacts only upon the set point value and does not require the measuredvalues of the variables. As a result, the feedforward signal can enhancecontrol speed in tracking the set point change. The most common methodof employing feedback is the traditional approach ofProportional-Derivative-Integral (PID) algorithm, and is appropriate forthe combined approach.

Local controllers can be used in the implementation of the apparatus ofFIGS. 1 through 4, and they are found in large numbers in mid-size tolarge buildings and have sufficient memory and processing capability toremain cost effective. A control scheme can be provided that is simple,easy to implement, inexpensive, and that provides substantialenhancement in performance by coupling feedforward and feedbackalgorithms. This provides an improvement over the PID controller thatreact to a control affected by the dynamic response of the coil andvalve signal. In the feedforward block previously explained, staticcharacteristics of these devices are stored and updated.

Combining feedforward and feedback blocks is preferably done in one oftwo way. As a first option, shown in FIG. 4, a simple switch 50 can beused to set the control signal from the PID algorithm to zero whenever aset point change is noticed. This approach is identified as model 1.Only the feedforward block produces a control signal when the set pointis changed. The PID output is only added when the set point does notchange, which indicates that the system is under steady state. Thiscombination approach is based on the fact that feedback is onlyresponsible for the steady state error that will not be detected by theopen feedforward block. It is reasonable to expect a relatively smallsteady state error due to the uncertainties introduced with theidentification scheme, measurement and controller.

In the second approach, termed model 2 and shown in FIG. 5, the netcontroller output is the result of addition of the feedforward output,the integral and derivative portions of the PID output and thesubtraction of the proportional part of the PID output. The logicemployed here is that by subtracting the proportional output, thefeedback will remain inactive to any change in the set point. Thefeedback will only provide the integral and the derivative actionsallowing the controller to respond to the set point change by means offeedforward block.

Both combination models are simulated and compared to each other using asimple sequence of pressure control to illustrate the responses. Bothmodels performed well although model 1 performs slightly better comparedto model 2 in terms of both undershoot and response time, as shown inFIG. 13. For decreasing flow, the trend is exactly opposite to that forincreasing flow. The performance of the controller improvessignificantly with shorter sample times. The sample time is a functionof controller processing and communication speed and is often dictatedby the cost. It is preferred that the controller have a sample time of1/10 sec. or 10 samples per second.

The method of General Regression Neural Network (GRNN) effectivelyidentifies characteristics of HVAC components for subsequent use incontrols. The strength of the GRNN is apparent as it has demonstratedits ability to adapt to both linear and nonlinear relations using bothsimulated and measured sample observations. Unlike a traditionalregression equation, however, a priori knowledge of the relationship interms of an equation is not necessary for implementing the GRNN. Thenature of the GRNN algorithm allows the method to be imbedded in aneural network architecture which makes hardware implementationpossible. The smoothing parameter is the only variable that needs to beselected and it can be determined using the Holdout or other methods.

Since a small data set is needed for local HVAC control component, i.e.valves, dampers and heating coils characteristics, the GRNN provides auseful means of characterizing static performance of HVAC components foruse in a feedforward block coupled with the feedback controller.Although the output Y is treated in this paper as a scalar, multipleoutputs can be also handled by GRNN.

Based on the results using measured data, a conservative estimate of a6% error in identifying coil and valve characteristics with the GRNNmethod is reasonable. Hence, a control signal can be generated with anaverage accuracy of 8.8%. The feedback controller will be adequate togenerate a control signal in order to eliminate a residual error of lessthan 10%. Additionally, the feedback controller will require minimumtuning since the error range is anticipated to be in a fixed low range.

The combined model 1 shown in FIG. 4 which uses the PID controller understeady state only demonstrated better performance for simple roompressure control compared to Model 2 shown in FIG. 5. Model 1 showedimproved performance in terms of response time, oscillation andstability when compared to the model 2.

From the foregoing, it should be appreciated that a superior controllerhas been shown and described which has robust control and is simple,easy to implement, inexpensive and provides substantial enhancement inperformance by coupling feedforward and feedback control algorithms.

While various embodiments of the present invention have been shown anddescribed, it should be understood that other modifications,substitutions and alternatives are apparent to one of ordinary skill inthe art. Such modifications, substitutions and alternatives can be madewithout departing from the spirit and scope of the invention, whichshould be determined from the appended claims.

Various features of the invention are set forth in the appended claims.

What is claimed is:
 1. A controller for controlling the air pressurewithin a room in a building having at least one space adjacent to theroom, the building having a heating, ventilating and air conditioning(HVAC) system with a supply duct adapted to supply air to the room andan exhaust duct adapted to exhaust air from the room, the system havinga component for controlling the supply air flow into the room, the roomhaving at least one additional exhaust independent of the HVAC system,said apparatus comprising:a feedforward means for generating afeedforward control signal based on desired flow set points in thesupply duct and general exhaust duct and based on identifyingcharacteristics of the component, said component comprises a supplydamper/actuator and an exhaust damper/actuator, the characteristicscomprising the flow rate of air entering the room, the set point of theflow rate of air entering the room, the flow rate of exhaust air leavingthe room, the set point of the flow rate of the exhaust air leaving theroom, the authority of the supply damper/actuator, the authority of thegeneral exhaust damper/actuator and calculated system variables; afeedback means for generating a feedback control signal based onmeasured system variables; and means for combining the feedforwardcontrol signal and the feedback control signal to achieve control of thelocal component.
 2. A controller as defined in claim 1 wherein saididentifying characteristics of said damper are determined by a GeneralRegression Neural Network (GRNN).
 3. A controller as defined in claim 2wherein said identifying characteristics are determined by the equation:##EQU12##
 4. A controller for controlling a component comprising asupply damper/actuator and a general exhaust damper/actuator of abuilding heating, ventilation and air-conditioning (HVAC) fluiddistribution system that affects at least the air pressure in aparticular room, the room having an air supply duct and at least onegeneral air exhaust duct, the controller being adapted to control theair flow into and out of the room to maintain a predetermineddifferential pressure relative to an adjacent space in the building, thecontroller comprising: an identification means for periodicallyproducing identified characteristics of the component to be controlled,said identified characteristics comprising the authority of saiddamper/actuators;a feedforward means coupled to the identificationmeans, for generating a feedforward control signal based on control setpoints and the identified characteristics of the component, said controlset points being determined to be those which maintain the mass of theair entering the room substantially equal to the mass of the air beingexhausted from the room; a feedback means for generating a feedbackcontrol signal based on measured system variables; and means forcombining the feedforward control signal and the feedback signal toachieve control of the local component.
 5. A controller as defined inclaim 4 wherein said control set points comprise the supply air flowrate set point and the general exhaust air flow rate set point.
 6. Acontroller as defined in claim 5 wherein said control set points areadaptively changed as a function of changes in said identifiedcharacteristics.
 7. A controller as defined in claim 6 wherein saidsupply air flow rate set point (ν_(s)|sp) is determined from theequations: ##EQU13## and where: P_(s)|sp is the supply air pressure setpoint;T_(s)|sp is the supply air temperature set point; ν_(s)|sp is thesupply air flow rate set point; P_(ad)|sp is the infiltration airpressure set point; T_(ad)|sp is the infiltration air temperature setpoint; ν_(s)|sp is the infiltration flow rate set point; P.sub.|sp isthe room pressure set point; ν_(e)|sp is the exhaust flow rate setpoint; T.sub.|sp is the room temperature set point.
 8. A controller asdefined in claim 4 wherein said identifying characteristics of saiddamper are determined by a General Regression Neural Network (GRNN). 9.A controller as defined in claim 8 wherein said identifyingcharacteristics are determined by the equation: ##EQU14##